def test_vstheory_ser(self, M, snr, synced):
s = signals.SignalQAMGrayCoded(M, 2 ** 16)
off = {4: 6, 16: 13, 64: 18, 128: 20}
ss = impairments.change_snr(s, snr+off[M])
ser = ss.cal_ser(synced=synced)
ser_t = theory.ser_vs_es_over_n0_qam(10**((snr+off[M])/10), M)
npt.assert_allclose(ser, ser_t, rtol=0.3)
def test_vstheory_ser(self, M, snr, os):
s = signals.SignalQAMGrayCoded(M, 2 ** 16)
off = {4: 6, 16: 13, 64: 18, 128: 20}
s = s.resample(os, beta=0.1, renormalise=True)
ss = impairments.change_snr(s, snr+off[M])
ss = ss.resample(1, beta=0.1, renormalise=True)
ser = ss.cal_ser()
ser_t = theory.ser_vs_es_over_n0_qam(10**((snr+off[M])/10), M)
npt.assert_allclose(ser, ser_t, rtol=0.3)
def test_ser_vs_theory(self, shift, M, snr):
from qampy import theory, impairments
s = signals.SignalQAMGrayCoded(M, 10**5, nmodes=1)
ser_t = theory.ser_vs_es_over_n0_qam(10**(snr / 10), M)
if ser_t < 1e-4:
assert True
return
s2 = impairments.change_snr(s, snr)
s2 = np.roll(s2, shift, axis=-1)
ser = s2.cal_ser()
npt.assert_allclose(ser, ser_t, rtol=0.4)
def plot_setup_SNR(M,
N,
snrf,
snr,
ber,
ser,
evmf,
evm1,
evm_known,
gmi,
Q_fc,
t_ory=True):
fig = plt.figure()
ax1 = fig.add_subplot(231)
ax2 = fig.add_subplot(232)
ax3 = fig.add_subplot(233)
ax4 = fig.add_subplot(234)
ax5 = fig.add_subplot(235)
ax6 = fig.add_subplot(236)
ax1.set_title("BER vs SNR")
ax2.set_title("SER vs SNR")
ax3.set_title("BER vs EVM")
ax4.set_title("EVM vs SNR")
ax5.set_title("Q-factor vs SNR")
ax6.set_title("GMI vs SNR")
ax1.set_xlabel('SNR [dB]')
ax1.set_ylabel('BER [dB]')
ax1.set_yscale('log')
## ax1.set_xlim(0,20)
## ax1.set_ylim(1e-5,1)
ax2.set_xlabel('SNR [dB]')
ax2.set_ylabel('SER [dB]')
ax2.set_yscale('log')
## ax2.set_xlim(0,20)
## ax2.set_ylim(1e-5,1)
ax3.set_xlabel('EVM [dB]')
ax3.set_ylabel('BER [dB]')
ax3.set_yscale('log')
## ax3.set_xlim(-15,0)
## ax3.set_ylim(1e-6,1)
ax4.set_xlabel('SNR [dB]')
ax4.set_ylabel('EVM [dB]')
## ax4.set_xlim(0, 30)
## ax4.set_ylim(-30, 0)
ax5.set_xlabel('SNR [dB]')
ax5.set_ylabel('Q-factor [dB]')
ax5.set_yscale('log', base=10)
ax5.set_xscale('log', base=10)
ax6.set_xlabel('SNR [dB]')
ax6.set_ylabel('GMI')
ax1.grid(True)
ax2.grid(True)
ax3.grid(True)
ax4.grid(True)
ax5.grid(True)
ax6.grid(True)
c = ['b', 'r', 'g', 'c', 'k']
s = ['o', '<', 's', '+', 'd']
j = 0
if t_ory == True:
ax1.plot(snrf,
theory.ber_vs_es_over_n0_qam(10**(snrf / 10), M),
color=c[j + 1],
label="%d-QAM theory" % M)
ax2.plot(snrf,
theory.ser_vs_es_over_n0_qam(10**(snrf / 10), M),
color=c[j + 1],
label="%d-QAM theory" % M)
ax5.plot(
snrf,
20 * np.log10(
special.erfcinv(
theory.ber_vs_es_over_n0_qam(10**(snrf / 10), M) * 2) *
np.sqrt(2)),
color=c[j + 1],
label="%d-QAM theory" % M)
ax6.plot(snrf,
theory.cal_gmi(M, snrf, N=N),
color=c[j],
label="GMI theory")
ax1.plot(snr, ber, color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
ax2.plot(snr, ser, color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
ax3.plot(evmf,
theory.ber_vs_evm_qam(evmf, M),
color=c[j],
label="%d-QAM theory" % M)
## ax3.plot(qampy.helpers.lin2dB(evm1 ** 2), ber, color=c[j+1], marker=s[j], lw=0, label="%d-QAM" % M)
# illustrate the difference between a blind and non-blind EVM
ax3.plot(qampy.helpers.lin2dB(evm_known**2),
ber,
color=c[j + 2],
marker='*',
lw=0,
label="%d-QAM" % M) # non-blind" % M)
## ax4.plot(snr, qampy.helpers.lin2dB(evm1 ** 2), color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
ax4.plot(snr,
qampy.helpers.lin2dB(evm_known**2),
color=c[j + 1],
marker='*',
lw=0,
label="%d-QAM" % M) # non-blind" % M)
#ax4.plot(snr, utils.lin2dB(evm2), color=c[j], marker='*', lw=0, label="%d-QAM signalq"%M)
ax5.plot(snr, Q_fc, color=c[j], marker=s[j], lw=0, label="Q-factor")
ax6.plot(snr, gmi[:, 0], color=c[j], marker=s[j], lw=0, label="GMI")
ax1.legend()
ax2.legend()
ax3.legend()
ax4.legend()
ax5.legend()
ax6.legend()
signalafter = equalisation.apply_filter(signal_s, wx)
signalafter = helpers.normalise_and_center(signalafter)
evm1[i] = signal.cal_evm()[0]
evm_known[i] = signalafter.cal_evm()
# check to see that we can recovery timing delay
#signalafter = np.roll(signalafter * 1.j**np.random.randint(0,4), np.random.randint(4, 3000))
ser[i] = signalafter.cal_ser()
ber[i] = signalafter.cal_ber()
i += 1
ax1.plot(snrf,
theory.ber_vs_es_over_n0_qam(10**(snrf / 10), M),
color=c[j],
label="%d-QAM theory" % M)
ax1.plot(snr, ber, color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
ax2.plot(snrf,
theory.ser_vs_es_over_n0_qam(10**(snrf / 10), M),
color=c[j],
label="%d-QAM theory" % M)
ax2.plot(snr, ser, color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
ax3.plot(evmf,
theory.ber_vs_evm_qam(evmf, M),
color=c[j],
label="%d-QAM theory" % M)
ax3.plot(qampy.helpers.lin2dB(evm1**2),
ber,
color=c[j],
marker=s[j],
lw=0,
label="%d-QAM" % M)
# illustrate the difference between a blind and non-blind EVM
ax3.plot(qampy.helpers.lin2dB(evm_known**2),
signal = signal.resample(fnew=fs, beta=beta, renormalise=True)
signal_s = impairments.change_snr(signal, sr)
#signalx = np.atleast_2d(filtering.rrcos_pulseshaping(signal_s, beta))
wx, er = equalisation.equalise_signal(signal_s, 3e-4, Ntaps=ntaps, method="mcma", adaptive_step=True, avoid_cma_sing=False)
signalafter = equalisation.apply_filter(signal_s, wx )
signalafter = helpers.normalise_and_center(signalafter)
evm1[i] = signal.cal_evm()[0]
evm_known[i] = signalafter.cal_evm()
# check to see that we can recovery timing delay
#signalafter = np.roll(signalafter * 1.j**np.random.randint(0,4), np.random.randint(4, 3000))
ser[i] = signalafter.cal_ser()
ber[i] = signalafter.cal_ber()
i += 1
ax1.plot(snrf, theory.ber_vs_es_over_n0_qam(10 ** (snrf / 10), M), color=c[j], label="%d-QAM theory" % M)
ax1.plot(snr, ber, color=c[j], marker=s[j], lw=0, label="%d-QAM"%M)
ax2.plot(snrf, theory.ser_vs_es_over_n0_qam(10 ** (snrf / 10), M), color=c[j], label="%d-QAM theory" % M)
ax2.plot(snr, ser, color=c[j], marker=s[j], lw=0, label="%d-QAM"%M)
ax3.plot(evmf, theory.ber_vs_evm_qam(evmf, M), color=c[j], label="%d-QAM theory" % M)
ax3.plot(qampy.helpers.lin2dB(evm1 ** 2), ber, color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
# illustrate the difference between a blind and non-blind EVM
ax3.plot(qampy.helpers.lin2dB(evm_known ** 2), ber, color=c[j], marker='*', lw=0, label="%d-QAM non-blind" % M)
ax4.plot(snr, qampy.helpers.lin2dB(evm1 ** 2), color=c[j], marker=s[j], lw=0, label="%d-QAM" % M)
ax4.plot(snr, qampy.helpers.lin2dB(evm_known ** 2), color=c[j], marker='*', lw=0, label="%d-QAM non-blind" % M)
#ax4.plot(snr, utils.lin2dB(evm2), color=c[j], marker='*', lw=0, label="%d-QAM signalq"%M)
j += 1
ax1.legend()
ax2.legend()
#ax3.legend()
ax4.legend()
plt.show()
